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| 74 | << prev file | <a href="a_timecorrelate.html">next file >></a> <a href="a_correlate2d.html" target="_TOP">view single page</a> | <a href="./../../index.html?format=raw" target="_TOP">view frames</a> summary: fields | <a href="#routine_summary">routine</a> details: <a href="#routine_details">routine</a> |
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| 76 | </div> |
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| 79 | <div id="container"> |
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| 80 | |
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| 81 | <h1 class="directory"><a href="directory-overview.html?format=raw">ToBeReviewed/STATISTICS/</a></h1> |
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| 82 | <h2 class="pro_file">a_correlate2d.pro</h2> |
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| 83 | |
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| 84 | <div id="file_attr"> |
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| 85 | <dl> |
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| 86 | </dl> |
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| 87 | </div> |
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| 89 | <div id="file_comments"></div> |
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| 97 | |
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| 98 | <div id="routine_summary"> |
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| 99 | <h2>Routine summary</h2> |
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| 100 | |
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| 101 | <dl> |
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| 102 | |
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| 103 | <dt><p><a href="#_Auto_Cov2d"><span class="result">result = </span>Auto_Cov2d(<span class="result">X, Lag</span>, Double=<span class="result">Double</span>, zero2nan=<span class="result">zero2nan</span>)</a></p><dt> |
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| 104 | <dd> NAME: A_CORRELATE2d PURPOSE: This function computes the autocorrelation Px(K,L) or autocovariance Rx(K,L) of a sample population X[nx,ny] as a function of the lag (K,L).</dd> |
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| 105 | |
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| 106 | <dt><p><a href="#_A_Correlate2d"><span class="result">result = </span>A_Correlate2d(<span class="result">X, Lag</span>, Covariance=<span class="result">Covariance</span>, Double=<span class="result">Double</span>)</a></p><dt> |
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| 107 | <dd></dd> |
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| 108 | |
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| 109 | </dl> |
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| 110 | </div> |
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| 111 | |
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| 112 | |
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| 113 | <div id="routine_details"> |
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| 114 | |
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| 115 | |
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| 116 | <div class="routine_details" id="_Auto_Cov2d"> |
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| 117 | |
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| 118 | <h2><a class="top" href="#container">top</a>Auto_Cov2d </h2> |
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| 119 | |
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| 120 | <p class="header"> |
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| 121 | <span class="result">result = </span>Auto_Cov2d(<span class="result"><a href="#_Auto_Cov2d_param_X">X</a>, <a href="#_Auto_Cov2d_param_Lag">Lag</a></span>, <a href="#_Auto_Cov2d_keyword_Double">Double</a>=<span class="result">Double</span>, <a href="#_Auto_Cov2d_keyword_zero2nan">zero2nan</a>=<span class="result">zero2nan</span>)</p> |
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| 122 | |
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| 123 | <div class="comments"> |
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| 124 | NAME: |
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| 125 | A_CORRELATE2d |
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| 126 | |
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| 127 | PURPOSE: |
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| 128 | |
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| 129 | This function computes the autocorrelation Px(K,L) or |
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| 130 | autocovariance Rx(K,L) of a sample population X[nx,ny] as a |
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| 131 | function of the lag (K,L). |
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| 132 | |
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| 133 | CATEGORY: |
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| 134 | Statistics. |
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| 135 | |
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| 136 | CALLING SEQUENCE: |
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| 137 | Result = a_correlate2d(X, Lag) |
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| 138 | |
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| 139 | INPUTS: |
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| 140 | X: an 2 dimension Array [nx,ny] |
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| 141 | |
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| 142 | LAG: 2-element vector, in the intervals [-(nx-2), (nx-2)],[-(ny-2), (ny-2)], |
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| 143 | of type integer that specifies the absolute distance(s) between |
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| 144 | indexed elements of X. |
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| 145 | |
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| 146 | KEYWORD PARAMETERS: |
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| 147 | COVARIANCE: If set to a non-zero value, the sample autocovariance |
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| 148 | is computed. |
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| 149 | |
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| 150 | DOUBLE: If set to a non-zero value, computations are done in |
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| 151 | double precision arithmetic. |
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| 152 | |
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| 153 | EXAMPLE: |
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| 154 | |
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| 155 | PROCEDURE: |
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| 156 | |
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| 157 | |
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| 158 | nx-k-1 ny-l-1 |
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| 159 | sigma sigma (X[i,j]-Xmean)(X[i+k,j+l]-Ymean) |
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| 160 | i=0 j=0 |
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| 161 | correlation(X,[k,l])=------------------------------------------------------ |
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| 162 | nx-1 ny-1 |
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| 163 | sigma sigma (X[i,j]-Xmean)^2) |
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| 164 | i=0 j=0 |
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| 165 | |
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| 166 | |
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| 167 | nx-k-1 ny-l-1 |
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| 168 | sigma sigma (X[i,j]-Xmean)(Y[i+k,j+l]-Ymean) |
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| 169 | i=0 j=0 |
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| 170 | covariance(X,[k,l])=------------------------------------------------------ |
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| 171 | nx*ny |
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| 172 | |
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| 173 | Where Xmean is the mens of the sample population |
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| 174 | x=(x[0,0],x[1,0],...,x[nx-1,ny-1]). |
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| 175 | |
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| 176 | |
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| 177 | REFERENCE: |
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| 178 | |
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| 179 | MODIFICATION HISTORY:</div> |
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| 180 | |
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| 181 | |
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| 182 | |
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| 183 | |
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| 184 | <h3>Parameters</h3> |
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| 185 | |
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| 186 | |
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| 187 | <h4 id="_Auto_Cov2d_param_X">X |
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| 196 | </h4> |
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| 198 | <div class="comments"></div> |
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| 199 | |
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| 200 | <h4 id="_Auto_Cov2d_param_Lag">Lag |
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| 209 | </h4> |
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| 218 | <h3>Keywords</h3> |
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| 220 | <h4 id="_Auto_Cov2d_keyword_Double">Double |
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| 229 | </h4> |
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| 231 | <div class="comments"></div> |
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| 233 | <h4 id="_Auto_Cov2d_keyword_zero2nan">zero2nan |
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| 271 | </div> |
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| 272 | |
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| 273 | |
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| 274 | <div class="routine_details" id="_A_Correlate2d"> |
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| 275 | |
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| 276 | <h2><a class="top" href="#container">top</a>A_Correlate2d </h2> |
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| 277 | |
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| 278 | <p class="header"> |
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| 279 | <span class="result">result = </span>A_Correlate2d(<span class="result"><a href="#_A_Correlate2d_param_X">X</a>, <a href="#_A_Correlate2d_param_Lag">Lag</a></span>, <a href="#_A_Correlate2d_keyword_Covariance">Covariance</a>=<span class="result">Covariance</span>, <a href="#_A_Correlate2d_keyword_Double">Double</a>=<span class="result">Double</span>)</p> |
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| 280 | |
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| 281 | <div class="comments"></div> |
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| 286 | <h3>Parameters</h3> |
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| 287 | |
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| 288 | |
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| 289 | <h4 id="_A_Correlate2d_param_X">X |
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| 298 | </h4> |
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| 300 | <div class="comments"></div> |
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| 301 | |
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| 302 | <h4 id="_A_Correlate2d_param_Lag">Lag |
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| 320 | <h3>Keywords</h3> |
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| 321 | |
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| 322 | <h4 id="_A_Correlate2d_keyword_Covariance">Covariance |
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| 331 | </h4> |
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| 335 | <h4 id="_A_Correlate2d_keyword_Double">Double |
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| 379 | <div id="tagline">Produced by IDLdoc 2.0.</div> |
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